import pywikibot import RevisionPuller as RP import SearchEngine as SE import PageProcessor as PP engine = SE.SearchEngine() processor = PP.PageProcessor() def get_readable_text_of_old_revision(page_title: str, rev_id: int): """ Returns a string containing a "readable" version of a revision :param page_title: A string of the page title :param rev_id: The revision number of the desired revision :return: A string of the revision's readable text """ page = engine.search(page_title, 1, "nearmatch")[0] return processor.getReadableText(RP.get_text_of_old_revision(page, rev_id)) def get_revisions(page_title: str, recent_to_oldest: bool = True, num_revisions=None, start_time: pywikibot.Timestamp = None, end_time: pywikibot.Timestamp = None): """ Returns the last (num_revisions) revisions from a given Wikipedia page If all revisions are desired use: get_latest_revisions(page) :param page_title: A string containing the title of the desired page :param recent_to_oldest: Set to false if we want the revisions in order of oldest to most recent :param num_revisions: The number of revisions to be grabbed (set to an integer to set limit to number of revisions grabbed)
parser.add_argument("--mode", choices=["train","eval","repr_code","search"], default='train', help="The mode to run. The `train` mode trains a model;" " the `eval` mode evaluate models in a test set " " The `repr_code/repr_desc` mode computes vectors" " for a code snippet or a natural language description with a trained model.") parser.add_argument("--gen", type=int, default='3', help="Number of GA generation") parser.add_argument("--chunk_size", type=int, default='20', help="Number of inputs") parser.add_argument("--mutation_rate", type=float, default='0.05', help="Mutation Rate") parser.add_argument("--verbose",action="store_true", default=True, help="Be verbose") return parser.parse_args() if __name__ == '__main__': args = parse_args() config = getattr(configs, 'config_'+args.model)() engine = SearchEngine.SearchEngine(args, config) ##### Define model ###### logger.info('Build Model') #tf.compat.v1.global_variables_initializer() model = getattr(models, args.model)(config) # initialize the model model.build() model.summary(export_path = "./output/{}/".format(args.model)) optimizer = config.get('training_params', dict()).get('optimizer', 'adam') model.compile(optimizer=optimizer) data_path = args.data_path+args.dataset+'/'
import sys from PySide2.QtCore import * from PySide2.QtGui import * from PySide2.QtWidgets import * from vmmpy import * sys.path.append("RemoteMemoryScanner") from SearchEngine import * from UserInterface import * if __name__ == "__main__": app = QApplication(sys.argv) search_engine = SearchEngine() user_interface = UserInterface(search_engine) user_interface.main_window.show() sys.exit(app.exec_())
def test_steam_miss(test_list): test_num = 200 auc_num = 0 alph = random.sample('qwertyuiopasdfghjklzxcvbnm', 1) for ids in test_list.keys(): game_name = test_list[ids] num = random.randint(0, len(game_name) - 1) change = game_name[num] new_name = game_name.replace(change, ''.join(alph), 1) results = get_search_results( "https://store.steampowered.com/search/?term=", new_name) if results is None: continue if len(results) > 10: results = results[:10] for game in results: if str(game['game_id']) == str(ids): auc_num += 1 break print('test_num:', test_num, 'accuracy', auc_num / test_num) if __name__ == "__main__": CACHE_DICT = open_cache() PS = PorterStemmer() search_engine = SearchEngine(config, word_tokenize, PS, isStemming=False) # test_list = search_engine.test_auc_zh_steam() # test_steam_miss(test_list) app.run(debug=True)